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About

About

João Gama is a Full Professor at the Faculty of Economy, University of Porto. He is a researcher and vice-director of LIAAD, a group belonging to INESC TEC. He got the PhD degree from the University of Porto, in 2000. He is a IEEE Fellow, EurIA Fellow, and member of the Academia de Ciências de Lisboa.

He has worked on several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc. He served as Co-Program chair of ECML'2005, DS'2009, ADMA'2009, IDA' 2011, ECMLPKDD'2015, and ECMLPKDD 2025. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECML/PKDD, and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is the author of several books on Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams. He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS. He (co-)supervised more than 12 PhD students and 50 MSc students.

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Details

Details

  • Name

    João Gama
  • Role

    Research Coordinator
  • Since

    01st January 2025
022
Publications

2026

Building of transformer-based RUL predictors supported by explainability techniques: Application on real industrial datasets

Authors
Dintén, R; Zorrilla, M; Veloso, B; Gama, J;

Publication
INFORMATION FUSION

Abstract
One of the key aspects of Industry 4.0 is using intelligent systems to optimize manufacturing processes by improving productivity and reducing costs. These systems have greatly impacted in different areas, such as demand prediction and quality assessment. However, the prognostics and health management of industrial equipment is one of the areas with greater potential. This paper presents a comparative analysis of deep learning architectures applied to the prediction of the remaining useful life (RUL) on public real industrial datasets. The analysis includes some of the most commonly employed recurrent neural network variations and a novel approach based on a hybrid architecture using transformers. Moreover, we apply explainability techniques to provide comprehensive insights into the model's decision-making process. The contributions of the work are: (1) a novel transformer-based architecture for RUL prediction that outperforms traditional recurrent neural networks; (2) a detailed description of the design strategies used to construct the models on two under-explored datasets; (3) the use of explainability techniques to understand the feature importance and to explain the model's prediction and (4) making models built for reproducibility available to other researchers.

2026

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX

Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (9)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part VIII

Authors
Pfahringer, B; Japkowicz, N; Larrañaga, P; Ribeiro, RP; Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (8)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part X

Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Pasquali, A; Moniz, N; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (10)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part VII

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (7)

Abstract